AI often goes into a product because "everyone is doing it", and six months later nobody can say what it did for the business. We treat AI as a working tool: it should save hours, lift conversion or make a process cheaper. Below are five use cases where that result can be measured, and how to test an idea without overspending.
In short
- AI pays off where there is repetitive work with text, data or images, and a clear way to measure the result.
- The most practical use cases are a support assistant, smarter search, document processing, content drafts and visual features.
- Start with one use case, measure the gain and the cost in a pilot, and scale only what works.
Five use cases with a measurable result
Most useful AI features today run on large language models (LLMs), systems that understand and generate text. You don't train one from scratch: it connects to your product through an API (a programming interface) and is set up for your data.
- A support assistant that answers from your knowledge base. The model first finds the relevant passages in your guides or FAQ, then writes an answer based on them, an approach called retrieval-augmented generation (RAG). The assistant handles routine questions around the clock and hands complex ones to a person, with the full conversation attached.
- Smarter search and recommendations. Semantic search looks for meaning rather than exact words: "something warm for autumn" finds jumpers and coats even if the description never says "warm". The same technology suggests similar products or articles, which helps people find what they need and buy it.
- Processing documents and data. AI pulls fields such as the amount, date or order number out of invoices, forms, contracts and emails, and passes them to your CRM (customer relationship management system) or accounting software. A person only checks the doubtful cases instead of retyping everything.
- Content drafts and personalisation, with a human review step. The model drafts product descriptions, emails or social media replies and adapts them to each audience segment. An editor has the final word on facts, tone and brand fit, so the team edits instead of starting from a blank page.
- Image generation and visual features. Generative models create and edit images: a background for a product photo, banner variations, an outfit previewed on a model. One example from our portfolio is Modella, an AI virtual try-on platform. They pay off when the image directly affects the decision to buy.
How to tell whether a use case pays off
Before you build anything, name the one metric the AI should change. Usually it is time saved, conversion or cost per request.
Time saved is the easiest to work out: multiply the minutes one task takes by the number of tasks a month, and compare that with the time needed to check the AI's output.
Conversion is measured with an A/B test: some users see the new feature and some don't. That shows whether a feature really brings in more orders or just pleases the team.
Cost per request is the price of one answer or one processed document. Most AI providers charge for the text going in and out, measured in tokens (small pieces of words). Add development and support on top. If the total is below the cost of people doing the same work, at acceptable quality, the use case makes sense.
The risks and how to handle them
Every AI feature brings new risks, and they need managing from day one.
- Wrong answers. Language models sometimes confidently make up facts, which is called hallucination. Limit the assistant to your knowledge base, have it show the source of each answer and let it say "I don't know".
- Data privacy. Before you connect a provider, find out where the data is processed, how long it is kept and whether it is used to train models. Personal data of customers in the EU falls under the General Data Protection Regulation (GDPR), so send the model only what it needs to answer.
- Usage costs. The bill grows with the number of requests. Set limits, store ready answers to common questions (caching) and use the smallest model that does the job well.
- A human in the loop. For decisions involving money, contracts or health, the AI prepares an option and a person approves it. That is slower than full automation, but far safer.
Start small, with one use case
The most expensive mistake is building an "AI platform" for everything at once. It is far more reliable to pick one process with a visible problem and a result that is easy to measure.
Then run a pilot for a few weeks with real data and a limited group of users. By the end, you know what one request costs, how often the model is wrong and how much time the team really saves. If the numbers add up, scale it and move on to the next one. If they don't, you have lost a few weeks, not a year's budget.
Checklist
- Pick one process where AI should save time, lift conversion or cut costs.
- Define the success metric and record its current value before you start.
- Work out the cost of one request, including development and support.
- Limit the model's answers to your knowledge base and verified data.
- Check where the provider processes and stores your data.
- Keep a person at the approval step for important decisions.
- Run a pilot on real data before you scale.
How we do it at IANKO
We build AI products the same way as web platforms and mobile apps. At the "find the purpose" stage, our Business Analyst works with you to define the metric the AI should change and to check whether it will pay off. Our Chief Technology Officer (CTO) chooses the model and the architecture, and our QA engineers and testers check the answers against real scenarios, including those where the model should honestly say "I don't know". At the "make it better" stage, we track the quality and cost of requests and refine the feature based on real use.
The bottom line
AI pays off not when it is simply in your product, but when it changes a specific metric: time, conversion or the cost of an operation. Pick one of these five use cases, work out the payoff and run a pilot, so you know whether to go further before the big spending starts.